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Data Analysis and Statistical Inference


Provider
coursera

Price
Free

School
Duke University

Type
University

Instructor
Mine Çetinkaya-Rundel

Categories
Mathematics, Education

Duration
10 weeks

Format
Video

Language
English

Description
The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This Specialization consists of 4 courses and a capstone project. The courses can be taken separately: Introduction to Probability and Data (began in April 2016) Inferential Statistics (begins in May 2016) Linear Regression and Modeling (begins in June 2016) Bayesian Statistics (begins in July 2016) A completely new course, with additional faculty! Statistics Capstone Project (August 2016) (for learners who have passed the 4 previous courses, and earned certificate)You may enroll in a single course, or all of them, but each requires the knowledge and techniques from the previous courses. The assignments in these courses have suggested but not required deadlines, so you can work at your own schedule. Please check the Specialization page for other answers to your questions, and peek at the first course. We hope to see you in our new courses. The Statistics with R team. ___________________________________________________ The goals of this course are as follows: Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference. Use statistical software (R) to summarize data numerically and visually, and to perform data analysis. Have a conceptual understanding of the unified nature of statistical inference. Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions. Model and investigate relationships between two or more variables within a regression framework. Interpret results correctly, effectively, and in context without relying on statistical jargon. Critique data-based claims and evaluate data-based decisions. Complete a research project that employs simple statistical inference and modeling techniques.